Automatic setup and deploy a MLflow server. This includes:

• A MLflow server
• A Postgres database to store MLflow metadata, such as:
  • experiment data;
  • run data;
  • model registry data.
• Minio, an open-source object storage system, to store and retrieve large files, such as:
  • model artifacts;
  • data sets.

• Miniconda3
• Docker
• Docker Compose

You could use WSL2 on a Windows machine, as an alternative to an Ubuntu machine.

Pyenv is used with MLflow to manage different Python versions and packages in isolated environments.

• Remove previous installations (optional):

  rm -rf ~/.pyenv

• Install any necessary package:

  sudo apt-get update -y
  sudo apt-get install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncursesw5-dev xz-utils tk-dev libxml2-dev libxmlsec1-dev libffi-dev liblzma-dev

• Automatic install pyenv:

  curl https://pyenv.run | bash

• Edit the ~/.bashrc file to recognize pyenv

  sudo nano ~/.bashrc

• And copy/paste the following line into the end of the file:

  # Config for PyEnv
  export PYENV_ROOT="$HOME/.pyenv"
  export PATH="$PYENV_ROOT/bin:$PATH"
  eval "$(pyenv init --path)"

• Save/close (Crtl-X > y > Enter), and then refresh the ~/.bashrc file:

  source ~/.bashrc

• Create and activate your MLflow experiment environment:

  conda create -n mlflow_env python=3.10
  conda activate mlflow_env
  pip install pandas scikit-learn mlflow[extras]

• Add these to the environment; edit to your own preferred secrets:

  export AWS_ACCESS_KEY_ID=minio
  export AWS_SECRET_ACCESS_KEY=minio123
  export MLFLOW_S3_ENDPOINT_URL=http://localhost:9000

• Clone this repo and navigate inside:

  git clone https://github.com/pandego/mlflow-postgres-minio.git
  cd ./mlflow-postgres-minio

• Edit default.env to your own preferred secrets:

• Launch the docker-compose command to build and start all containers needed for the MLflow service:

  docker compose --env-file default.env up -d --build

• Give it a few minutes and once docker-compose is finished, check the containers health:

  docker ps

• You should see something like this:

  

• You should also be able to navigate to:

  • The MLflow UI -> http://localhost:5000
  • The Minio UI -> http://localhost:9000

That's it! 🥳 You can now start using MLflow!

• From your previously created MLflow environment, mlflow_env, simply run the example provided in this repo:

  python ./wine_quality_example/wine_quality_example.py

• From your previously created MLflow environment, mlflow_env, serve the model by running the following command, replacing the <full_path> for your own:

  mlflow models serve -m <full_path> -h 127.0.0.1 -p 1234 --timeout 0 

  You just have to replace <full_path> by the full path to your model artifact as provided in the MLFlow web UI.
  

• Let it run, it should look like this:

  

Mlflow also allows you to build a dockerized API based on a model stored in one of your runs.

• The following command allows you to build this dockerzed API:

  mlflow models build-docker \
      -m <full_path> \ 
      -n adorable-mouse \ 
      --enable-mlserver

• All is left to do is to run this container:

  docker run -p 1234:8080 adorable-mouse

• In case you just want to generate a docker file for later use, use the following command:

  mlflow models generate-dockerfile \ 
      -m <full_path> \ 
      -d ./adorable-mouse \ 
      --enable-mlserver

• You can then include it in a docker-compose.yml:

  version: '3.8'
  
  services:
    mlflow-model-serve-from-adorable-mouse:
      build: ./adorable-mouse
      image: adorable-mouse
      container_name: adorable-mouse_instance
      restart: always
      ports:
        - 1234:8080
      healthcheck:
        test: ["CMD", "curl", "-f", "http://localhost:8080/health/"]
        interval: 30s
        timeout: 10s
        retries: 3

• You can run this docker-compose.yml with the following command:

  docker compose up

• In a different terminal, send a request to test the served model:

  curl -X POST -H "Content-Type: application/json" --data '{"dataframe_split": {"data": [[7.4,0.7,0,1.9,0.076,11,34,0.9978,3.51,0.56,9.4]], "columns": ["fixed acidity","volatile acidity","citric acid","residual sugar","chlorides","free sulfur dioxide","total sulfur dioxide","density","pH","sulphates","alcohol"]}}' http://127.0.0.1:1234/invocations

• The output should be the something like the following:

  $ {"predictions": [5.576883967129616]}

🎊 Et voilà! 🎊

• https://mlflow.org/docs/latest/quickstart.html
• https://github.com/mlflow/mlflow-example
• https://ghcr.io/mlflow/mlflow
• https://github.com/Gorcenski/ds-workbench